Target Estimation by Exploiting Low Rank Structure in Widely Separated MIMO Radar

We present a low-complexity widely separated multiple-input-multiple-output (WS-MIMO) radar that samples the signals at each of its multiple receivers at reduced rates. We process the low-rate samples of all transmit-receive chains at each receiver as data matrices. We demonstrate that each of these matrices is low rank as long as the target moves slowly within a coherent processing interval. We apply recent advances in matrix completion to recover the missing samples of each receiver signal matrix at the common fusion center. The maximum likelihood method is then employed to estimate the targets' positions and Doppler velocities. Unlike other WS-MIMO formulations based on compressed sensing, our approach recovers target parameters at reduced rates, and is gridless. Further, there is no loss of signal-to-noise ratio (SNR) due to subsampling. Numerical experiments demonstrate reasonably accurate target localization at SNR of 20 dB and sampling rate reduction to 20%.